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Data description Data parameters OrderNumber: customer nickname LineNumber: purchase order. For example, the first three lines respectively represent three goods purchased by the same customer Model: trade name Problem description Application of intelligent algorithm recommendation of association rules based on shopping basket. Three basic ...

Level 1: Statistics of user churn Task description This task: according to the user behavior data, write MapReduce program to count the loss of users. Relevant knowledge This training is an intermediate difficulty MapReduce programming exercise, which simulates the statistical analysis of e-commerce data in real scenes. Therefore, it is ...

Original link: http://tecdat.cn/?p=24498In this example, we consider Markov transformation stochastic volatility model.statistical modelGive Way    Are dependent variables and    Unobserved log volatility  The stochastic volatility model is defined as follows  Zone variable    Following a two-state Markov ...

1. Time series algorithm   1.1 differential autoregressive moving average model (Arima) 1.1.1 overview          ARIMA is a typical time series model, which consists of three parts: AR model (autoregressive model) and MA model (moving average model), as well as the order I of difference. Therefore, ...

Official account: Special HouseAuthor: PeterEditor: Peter Hello, I'm Peter~ This article mainly introduces how to beautify the data of Pandas DataFrame. It is mainly realized through two methods in Pandas: Styler.applymap: returns a single string with CSS attribute value pairs element by elementStyler.apply: returns Series or DataFrame wit ...

1. Model construction 1.1 get modeling data #Read raw data train = pd.read_csv('train.csv') #Read cleaned data set data = pd.read_csv('clear_data.csv') 1.2 select appropriate model Before model selection, we need to know whether the data set is finally supervised learning or unsupervised learning Machine learning is mainly divided into ...

1, Introduction to KMeans algorithm: K in the name of KMeans algorithm represents the number of categories, and Means represents the mean value of samples in each category. Therefore, KMeans algorithm is also called k-Means algorithm. KMeans algorithm takes distance as the measure of similarity between samples, and assigns samples with similar ...

preface Although the code is often very long, it is annotation for understanding 1, KMeans KMeans can be said to be one of the simplest clustering algorithms 1.1 how does kmeans work Key concepts: cluster and centroid KMeans algorithm divides the characteristic matrix X of a group of N samples into K clusters without inters ...

Hello, everyone. I'm a studious junior brother. Today, I will continue to explain the second method I wrote: etree method based on lxml library combined with xpath method - crawling the contents of the ranking list and generating the word cloud map. The learning experience is mainly divided into three lectures:         ...

Title Description Given the training data set (pictures of cats), let's build a simple neural network to identify cats. Dataset description There are 209 pictures in the training set, and the shape of each picture is (64, 64, 3) There are 50 pictures in the test set, and the shape of each picture is (64, 64, 3) classes stores two string data ...